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Point-of-Interest recommendation is an increasing research and developingarea within the widely adopted technologies known as Recommender Systems. Amongthem, those that exploit information coming from Location-Based Social Networks(LBSNs) are very popular nowadays and could work with different informationsources, which pose several challenges and research questions to the communityas a whole. We present a systematic review focused on the research done in thelast 10 years about this topic. We discuss and categorize the algorithms andevaluation methodologies used in these works and point out the opportunitiesand challenges that remain open in the field. More specifically, we report theleading recommendation techniques and information sources that have beenexploited more often (such as the geographical signal and deep learningapproaches) while we also alert about the lack of reproducibility in the fieldthat may hinder real performance improvements.

Point-of-Interest Recommender Systems based on Location-Based Social Networks: A Survey from an Experimental Perspective